Developing an AI application¶
Going forward, AI algorithms will be incorporated into more and more everyday applications. For example, you might want to include an image classifier in a smart phone app. To do this, you'd use a deep learning model trained on hundreds of thousands of images as part of the overall application architecture. A large part of software development in the future will be using these types of models as common parts of applications.
In this project, you'll train an image classifier to recognize different species of flowers. You can imagine using something like this in a phone app that tells you the name of the flower your camera is looking at. In practice you'd train this classifier, then export it for use in your application. We'll be using this dataset of 102 flower categories, you can see a few examples below.
The project is broken down into multiple steps:
- Load and preprocess the image dataset
- Train the image classifier on your dataset
- Use the trained classifier to predict image content
We'll lead you through each part which you'll implement in Python.
When you've completed this project, you'll have an application that can be trained on any set of labeled images. Here your network will be learning about flowers and end up as a command line application. But, what you do with your new skills depends on your imagination and effort in building a dataset. For example, imagine an app where you take a picture of a car, it tells you what the make and model is, then looks up information about it. Go build your own dataset and make something new.
First up is importing the packages you'll need. It's good practice to keep all the imports at the beginning of your code. As you work through this notebook and find you need to import a package, make sure to add the import up here.
Imports¶
In [1]:
# Imports here
import seaborn as sns
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
import matplotlib.pyplot as plt
import torch
from torch import nn
from torch import optim
import torch.nn.functional as F
from torchvision import datasets, transforms, models
import numpy as np
from PIL import Image
import pandas as pd
import os
os.getcwd()
import glob
import json
# Udacity file had a hyphen in its name. I renamed the file from workspace-utils.py to workspace_utils.py
from workspace_utils import active_session
In [2]:
# import helper
# ---------------------------------------------------------------------------
# ModuleNotFoundError Traceback (most recent call last)
# <ipython-input-89-b1da2f10f7bc> in <module>()
# ----> 1 import helper
# ModuleNotFoundError: No module named 'helper'
Load the data¶
Here you'll use torchvision to load the data (documentation). The data should be included alongside this notebook, otherwise you can download it here. The dataset is split into three parts, training, validation, and testing. For the training, you'll want to apply transformations such as random scaling, cropping, and flipping. This will help the network generalize leading to better performance. You'll also need to make sure the input data is resized to 224x224 pixels as required by the pre-trained networks.
The validation and testing sets are used to measure the model's performance on data it hasn't seen yet. For this you don't want any scaling or rotation transformations, but you'll need to resize then crop the images to the appropriate size.
The pre-trained networks you'll use were trained on the ImageNet dataset where each color channel was normalized separately. For all three sets you'll need to normalize the means and standard deviations of the images to what the network expects. For the means, it's [0.485, 0.456, 0.406] and for the standard deviations [0.229, 0.224, 0.225], calculated from the ImageNet images. These values will shift each color channel to be centered at 0 and range from -1 to 1.
In [2]:
# directories
data_dir = 'flowers'
train_dir = data_dir + '/train'
valid_dir = data_dir + '/valid'
test_dir = data_dir + '/test'
In [3]:
# TODO: Define your transforms for the training, validation, and testing sets
"""
Source: Udacity Deep Learning with Pytorch lesson
"""
train_transforms = transforms.Compose([transforms.RandomRotation(30),
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406],
[0.229, 0.224, 0.225])])
valid_transforms = transforms.Compose([transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406],
[0.229, 0.224, 0.225])])
test_transforms = transforms.Compose([transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406],
[0.229, 0.224, 0.225])])
# TODO: Load the datasets with ImageFolder
train_data = datasets.ImageFolder(train_dir, transform=train_transforms)
valid_data = datasets.ImageFolder(valid_dir, transform=valid_transforms)
test_data = datasets.ImageFolder(test_dir, transform=test_transforms)
# TODO: Using the image datasets and the trainforms, define the dataloaders
trainloader = torch.utils.data.DataLoader(train_data, batch_size=64, shuffle=True)
validloader = torch.utils.data.DataLoader(valid_data, batch_size=64, shuffle=True)
testloader = torch.utils.data.DataLoader(test_data, batch_size=64, shuffle=True)
In [4]:
# https://discuss.pytorch.org/t/about-the-relation-between-batch-size-and-length-of-data-loader/10510/2
# my understanding is that len(dataloader) = len(dataset) / batch_size
print('length of data loaders')
print(len(trainloader))
print(len(validloader))
print(len(testloader))
length of data loaders 103 13 13
In [40]:
# this looks like the size of train set
103*64
Out[40]:
6592
In [45]:
# This looks like the size of test set. Valid data has the same size
13*64
Out[45]:
832
In [44]:
# (valid_data + test_data) / all_data
832*2/(6592+832*2)
Out[44]:
0.20155038759689922
In [47]:
# (test_data) / all_data
832/(6592+832*2)
Out[47]:
0.10077519379844961
In [5]:
def show_Image(loader):
"""
credit: adapted from https://github.com/Surya-Prakash-Reddy/Classifying-Cats-and-Dogs
by Surya Prakash Reddy
"""
data_iter = iter(loader)
images, labels = next(data_iter)
img = images[0].cpu().numpy().transpose(1,2,0)
mean = np.array([0.485, 0.456, 0.406])
std = np.array([0.229, 0.224, 0.225])
img = std * img + mean
img = np.clip(img, 0, 1)
plt.imshow(img)
In [12]:
show_Image(trainloader)
In [13]:
show_Image(validloader)
In [14]:
show_Image(testloader)
Label mapping¶
You'll also need to load in a mapping from category label to category name. You can find this in the file cat_to_name.json. It's a JSON object which you can read in with the json module. This will give you a dictionary mapping the integer encoded categories to the actual names of the flowers.
In [6]:
with open('cat_to_name.json', 'r') as f:
cat_to_name = json.load(f)
In [9]:
cat_to_name['1']
Out[9]:
'pink primrose'
In [10]:
cat_to_name
Out[10]:
{'21': 'fire lily',
'3': 'canterbury bells',
'45': 'bolero deep blue',
'1': 'pink primrose',
'34': 'mexican aster',
'27': 'prince of wales feathers',
'7': 'moon orchid',
'16': 'globe-flower',
'25': 'grape hyacinth',
'26': 'corn poppy',
'79': 'toad lily',
'39': 'siam tulip',
'24': 'red ginger',
'67': 'spring crocus',
'35': 'alpine sea holly',
'32': 'garden phlox',
'10': 'globe thistle',
'6': 'tiger lily',
'93': 'ball moss',
'33': 'love in the mist',
'9': 'monkshood',
'102': 'blackberry lily',
'14': 'spear thistle',
'19': 'balloon flower',
'100': 'blanket flower',
'13': 'king protea',
'49': 'oxeye daisy',
'15': 'yellow iris',
'61': 'cautleya spicata',
'31': 'carnation',
'64': 'silverbush',
'68': 'bearded iris',
'63': 'black-eyed susan',
'69': 'windflower',
'62': 'japanese anemone',
'20': 'giant white arum lily',
'38': 'great masterwort',
'4': 'sweet pea',
'86': 'tree mallow',
'101': 'trumpet creeper',
'42': 'daffodil',
'22': 'pincushion flower',
'2': 'hard-leaved pocket orchid',
'54': 'sunflower',
'66': 'osteospermum',
'70': 'tree poppy',
'85': 'desert-rose',
'99': 'bromelia',
'87': 'magnolia',
'5': 'english marigold',
'92': 'bee balm',
'28': 'stemless gentian',
'97': 'mallow',
'57': 'gaura',
'40': 'lenten rose',
'47': 'marigold',
'59': 'orange dahlia',
'48': 'buttercup',
'55': 'pelargonium',
'36': 'ruby-lipped cattleya',
'91': 'hippeastrum',
'29': 'artichoke',
'71': 'gazania',
'90': 'canna lily',
'18': 'peruvian lily',
'98': 'mexican petunia',
'8': 'bird of paradise',
'30': 'sweet william',
'17': 'purple coneflower',
'52': 'wild pansy',
'84': 'columbine',
'12': "colt's foot",
'11': 'snapdragon',
'96': 'camellia',
'23': 'fritillary',
'50': 'common dandelion',
'44': 'poinsettia',
'53': 'primula',
'72': 'azalea',
'65': 'californian poppy',
'80': 'anthurium',
'76': 'morning glory',
'37': 'cape flower',
'56': 'bishop of llandaff',
'60': 'pink-yellow dahlia',
'82': 'clematis',
'58': 'geranium',
'75': 'thorn apple',
'41': 'barbeton daisy',
'95': 'bougainvillea',
'43': 'sword lily',
'83': 'hibiscus',
'78': 'lotus lotus',
'88': 'cyclamen',
'94': 'foxglove',
'81': 'frangipani',
'74': 'rose',
'89': 'watercress',
'73': 'water lily',
'46': 'wallflower',
'77': 'passion flower',
'51': 'petunia'}
Building and training the classifier¶
Now that the data is ready, it's time to build and train the classifier. As usual, you should use one of the pretrained models from torchvision.models to get the image features. Build and train a new feed-forward classifier using those features.
We're going to leave this part up to you. Refer to the rubric for guidance on successfully completing this section. Things you'll need to do:
- Load a pre-trained network (If you need a starting point, the VGG networks work great and are straightforward to use)
- Define a new, untrained feed-forward network as a classifier, using ReLU activations and dropout
- Train the classifier layers using backpropagation using the pre-trained network to get the features
- Track the loss and accuracy on the validation set to determine the best hyperparameters
We've left a cell open for you below, but use as many as you need. Our advice is to break the problem up into smaller parts you can run separately. Check that each part is doing what you expect, then move on to the next. You'll likely find that as you work through each part, you'll need to go back and modify your previous code. This is totally normal!
When training make sure you're updating only the weights of the feed-forward network. You should be able to get the validation accuracy above 70% if you build everything right. Make sure to try different hyperparameters (learning rate, units in the classifier, epochs, etc) to find the best model. Save those hyperparameters to use as default values in the next part of the project.
One last important tip if you're using the workspace to run your code: To avoid having your workspace disconnect during the long-running tasks in this notebook, please read in the earlier page in this lesson called Intro to GPU Workspaces about Keeping Your Session Active. You'll want to include code from the workspace_utils.py module.
Note for Workspace users: If your network is over 1 GB when saved as a checkpoint, there might be issues with saving backups in your workspace. Typically this happens with wide dense layers after the convolutional layers. If your saved checkpoint is larger than 1 GB (you can open a terminal and check with ls -lh), you should reduce the size of your hidden layers and train again.
In [26]:
# TODO: Build and train your network
In [15]:
model = models.vgg16(pretrained=True)
print(model)
VGG(
(features): Sequential(
(0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): ReLU(inplace)
(2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(3): ReLU(inplace)
(4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(6): ReLU(inplace)
(7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(8): ReLU(inplace)
(9): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(11): ReLU(inplace)
(12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(13): ReLU(inplace)
(14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(15): ReLU(inplace)
(16): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(17): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(18): ReLU(inplace)
(19): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(20): ReLU(inplace)
(21): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(22): ReLU(inplace)
(23): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(24): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(25): ReLU(inplace)
(26): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(27): ReLU(inplace)
(28): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(29): ReLU(inplace)
(30): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
)
(classifier): Sequential(
(0): Linear(in_features=25088, out_features=4096, bias=True)
(1): ReLU(inplace)
(2): Dropout(p=0.5)
(3): Linear(in_features=4096, out_features=4096, bias=True)
(4): ReLU(inplace)
(5): Dropout(p=0.5)
(6): Linear(in_features=4096, out_features=1000, bias=True)
)
)
In [10]:
model = models.vgg19_bn(pretrained=True)
model
Out[10]:
VGG(
(features): Sequential(
(0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace)
(3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(5): ReLU(inplace)
(6): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(7): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(8): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(9): ReLU(inplace)
(10): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(11): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(12): ReLU(inplace)
(13): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(14): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(15): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(16): ReLU(inplace)
(17): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(18): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(19): ReLU(inplace)
(20): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(21): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(22): ReLU(inplace)
(23): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(24): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(25): ReLU(inplace)
(26): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(27): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(28): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(29): ReLU(inplace)
(30): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(31): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(32): ReLU(inplace)
(33): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(34): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(35): ReLU(inplace)
(36): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(37): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(38): ReLU(inplace)
(39): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(40): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(41): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(42): ReLU(inplace)
(43): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(44): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(45): ReLU(inplace)
(46): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(47): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(48): ReLU(inplace)
(49): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(50): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(51): ReLU(inplace)
(52): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
)
(classifier): Sequential(
(0): Linear(in_features=25088, out_features=4096, bias=True)
(1): ReLU(inplace)
(2): Dropout(p=0.5)
(3): Linear(in_features=4096, out_features=4096, bias=True)
(4): ReLU(inplace)
(5): Dropout(p=0.5)
(6): Linear(in_features=4096, out_features=1000, bias=True)
)
)
In [11]:
model.classifier
Out[11]:
Sequential( (0): Linear(in_features=25088, out_features=4096, bias=True) (1): ReLU(inplace) (2): Dropout(p=0.5) (3): Linear(in_features=4096, out_features=4096, bias=True) (4): ReLU(inplace) (5): Dropout(p=0.5) (6): Linear(in_features=4096, out_features=1000, bias=True) )
In [12]:
# Use GPU if it's available
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(device)
In [13]:
# Freeze parameters so we don't backprop through them
for param in model.parameters():
param.requires_grad = False
model.classifier = nn.Sequential(
nn.Linear(25088, 4096),
nn.ReLU(),
nn.Dropout(p=0.2),
nn.Linear(4096, 1024),
nn.ReLU(),
nn.Dropout(p=0.2),
nn.Linear(1024, 102),
nn.LogSoftmax(dim=1)
)
criterion = nn.NLLLoss()
# Only train the classifier parameters, feature parameters are frozen
optimizer = optim.Adam(model.classifier.parameters(), lr=0.001)
model.to(device)
Out[13]:
VGG(
(features): Sequential(
(0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace)
(3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(5): ReLU(inplace)
(6): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(7): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(8): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(9): ReLU(inplace)
(10): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(11): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(12): ReLU(inplace)
(13): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(14): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(15): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(16): ReLU(inplace)
(17): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(18): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(19): ReLU(inplace)
(20): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(21): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(22): ReLU(inplace)
(23): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(24): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(25): ReLU(inplace)
(26): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(27): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(28): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(29): ReLU(inplace)
(30): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(31): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(32): ReLU(inplace)
(33): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(34): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(35): ReLU(inplace)
(36): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(37): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(38): ReLU(inplace)
(39): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(40): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(41): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(42): ReLU(inplace)
(43): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(44): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(45): ReLU(inplace)
(46): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(47): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(48): ReLU(inplace)
(49): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(50): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(51): ReLU(inplace)
(52): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
)
(classifier): Sequential(
(0): Linear(in_features=25088, out_features=4096, bias=True)
(1): ReLU()
(2): Dropout(p=0.2)
(3): Linear(in_features=4096, out_features=1024, bias=True)
(4): ReLU()
(5): Dropout(p=0.2)
(6): Linear(in_features=1024, out_features=102, bias=True)
(7): LogSoftmax()
)
)
In [14]:
model
Out[14]:
VGG(
(features): Sequential(
(0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace)
(3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(5): ReLU(inplace)
(6): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(7): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(8): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(9): ReLU(inplace)
(10): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(11): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(12): ReLU(inplace)
(13): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(14): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(15): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(16): ReLU(inplace)
(17): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(18): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(19): ReLU(inplace)
(20): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(21): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(22): ReLU(inplace)
(23): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(24): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(25): ReLU(inplace)
(26): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(27): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(28): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(29): ReLU(inplace)
(30): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(31): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(32): ReLU(inplace)
(33): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(34): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(35): ReLU(inplace)
(36): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(37): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(38): ReLU(inplace)
(39): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(40): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(41): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(42): ReLU(inplace)
(43): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(44): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(45): ReLU(inplace)
(46): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(47): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(48): ReLU(inplace)
(49): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(50): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(51): ReLU(inplace)
(52): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
)
(classifier): Sequential(
(0): Linear(in_features=25088, out_features=4096, bias=True)
(1): ReLU()
(2): Dropout(p=0.2)
(3): Linear(in_features=4096, out_features=1024, bias=True)
(4): ReLU()
(5): Dropout(p=0.2)
(6): Linear(in_features=1024, out_features=102, bias=True)
(7): LogSoftmax()
)
)
In [15]:
"""
Code adapted from Udacity Deep Learning with Pytorch lesson
"""
epochs = 7
steps = 0
running_loss = 0
print_every = 5
with active_session():
for epoch in range(epochs):
for inputs, labels in trainloader:
steps += 1
# Move input and label tensors to the default device
inputs, labels = inputs.to(device), labels.to(device)
optimizer.zero_grad()
logps = model.forward(inputs)
loss = criterion(logps, labels)
loss.backward()
optimizer.step()
running_loss += loss.item()
if steps % print_every == 0:
valid_loss = 0
accuracy = 0
model.eval()
with torch.no_grad():
for inputs, labels in validloader:
inputs, labels = inputs.to(device), labels.to(device)
logps = model.forward(inputs)
batch_loss = criterion(logps, labels)
valid_loss += batch_loss.item()
# Calculate accuracy
ps = torch.exp(logps)
top_p, top_class = ps.topk(1, dim=1)
equals = top_class == labels.view(*top_class.shape)
accuracy += torch.mean(equals.type(torch.FloatTensor)).item()
print(f"Epoch {epoch+1}/{epochs}.. "
f"Train loss: {running_loss/print_every:.3f}.. "
f"Validation loss: {valid_loss/len(validloader):.3f}.. "
f"Validation accuracy: {accuracy/len(validloader):.3f}")
running_loss = 0
model.train()
Epoch 1/7.. Train loss: 4.806.. Validation loss: 4.552.. Validation accuracy: 0.066 Epoch 1/7.. Train loss: 4.574.. Validation loss: 4.420.. Validation accuracy: 0.062 Epoch 1/7.. Train loss: 4.405.. Validation loss: 4.279.. Validation accuracy: 0.089 Epoch 1/7.. Train loss: 4.247.. Validation loss: 4.016.. Validation accuracy: 0.129 Epoch 1/7.. Train loss: 4.095.. Validation loss: 3.775.. Validation accuracy: 0.179 Epoch 1/7.. Train loss: 3.801.. Validation loss: 3.478.. Validation accuracy: 0.219 Epoch 1/7.. Train loss: 3.609.. Validation loss: 3.224.. Validation accuracy: 0.266 Epoch 1/7.. Train loss: 3.270.. Validation loss: 2.835.. Validation accuracy: 0.321 Epoch 1/7.. Train loss: 3.119.. Validation loss: 2.577.. Validation accuracy: 0.391 Epoch 1/7.. Train loss: 3.022.. Validation loss: 2.402.. Validation accuracy: 0.411 Epoch 1/7.. Train loss: 2.634.. Validation loss: 2.028.. Validation accuracy: 0.476 Epoch 1/7.. Train loss: 2.345.. Validation loss: 1.783.. Validation accuracy: 0.528 Epoch 1/7.. Train loss: 2.285.. Validation loss: 1.721.. Validation accuracy: 0.531 Epoch 1/7.. Train loss: 2.325.. Validation loss: 1.573.. Validation accuracy: 0.563 Epoch 1/7.. Train loss: 2.211.. Validation loss: 1.499.. Validation accuracy: 0.587 Epoch 1/7.. Train loss: 2.066.. Validation loss: 1.495.. Validation accuracy: 0.579 Epoch 1/7.. Train loss: 2.016.. Validation loss: 1.421.. Validation accuracy: 0.611 Epoch 1/7.. Train loss: 2.134.. Validation loss: 1.338.. Validation accuracy: 0.632 Epoch 1/7.. Train loss: 1.725.. Validation loss: 1.242.. Validation accuracy: 0.646 Epoch 1/7.. Train loss: 1.639.. Validation loss: 1.171.. Validation accuracy: 0.649 Epoch 2/7.. Train loss: 1.656.. Validation loss: 1.076.. Validation accuracy: 0.680 Epoch 2/7.. Train loss: 1.710.. Validation loss: 1.068.. Validation accuracy: 0.686 Epoch 2/7.. Train loss: 1.502.. Validation loss: 1.018.. Validation accuracy: 0.707 Epoch 2/7.. Train loss: 1.337.. Validation loss: 0.993.. Validation accuracy: 0.716 Epoch 2/7.. Train loss: 1.353.. Validation loss: 0.877.. Validation accuracy: 0.731 Epoch 2/7.. Train loss: 1.349.. Validation loss: 0.947.. Validation accuracy: 0.728 Epoch 2/7.. Train loss: 1.424.. Validation loss: 0.888.. Validation accuracy: 0.753 Epoch 2/7.. Train loss: 1.419.. Validation loss: 0.869.. Validation accuracy: 0.752 Epoch 2/7.. Train loss: 1.367.. Validation loss: 0.865.. Validation accuracy: 0.761 Epoch 2/7.. Train loss: 1.368.. Validation loss: 0.904.. Validation accuracy: 0.723 Epoch 2/7.. Train loss: 1.496.. Validation loss: 0.834.. Validation accuracy: 0.754 Epoch 2/7.. Train loss: 1.388.. Validation loss: 0.894.. Validation accuracy: 0.738 Epoch 2/7.. Train loss: 1.389.. Validation loss: 0.833.. Validation accuracy: 0.741 Epoch 2/7.. Train loss: 1.251.. Validation loss: 0.806.. Validation accuracy: 0.787 Epoch 2/7.. Train loss: 1.326.. Validation loss: 0.731.. Validation accuracy: 0.802 Epoch 2/7.. Train loss: 1.276.. Validation loss: 0.646.. Validation accuracy: 0.811 Epoch 2/7.. Train loss: 1.147.. Validation loss: 0.636.. Validation accuracy: 0.832 Epoch 2/7.. Train loss: 1.198.. Validation loss: 0.651.. Validation accuracy: 0.821 Epoch 2/7.. Train loss: 1.191.. Validation loss: 0.667.. Validation accuracy: 0.809 Epoch 2/7.. Train loss: 1.110.. Validation loss: 0.693.. Validation accuracy: 0.797 Epoch 2/7.. Train loss: 1.324.. Validation loss: 0.637.. Validation accuracy: 0.820 Epoch 3/7.. Train loss: 1.232.. Validation loss: 0.627.. Validation accuracy: 0.829 Epoch 3/7.. Train loss: 1.053.. Validation loss: 0.670.. Validation accuracy: 0.807 Epoch 3/7.. Train loss: 1.068.. Validation loss: 0.597.. Validation accuracy: 0.837 Epoch 3/7.. Train loss: 1.104.. Validation loss: 0.596.. Validation accuracy: 0.828 Epoch 3/7.. Train loss: 1.030.. Validation loss: 0.566.. Validation accuracy: 0.842 Epoch 3/7.. Train loss: 1.064.. Validation loss: 0.516.. Validation accuracy: 0.859 Epoch 3/7.. Train loss: 1.113.. Validation loss: 0.549.. Validation accuracy: 0.839 Epoch 3/7.. Train loss: 0.944.. Validation loss: 0.593.. Validation accuracy: 0.835 Epoch 3/7.. Train loss: 0.970.. Validation loss: 0.573.. Validation accuracy: 0.825 Epoch 3/7.. Train loss: 1.053.. Validation loss: 0.475.. Validation accuracy: 0.863 Epoch 3/7.. Train loss: 0.889.. Validation loss: 0.484.. Validation accuracy: 0.859 Epoch 3/7.. Train loss: 0.999.. Validation loss: 0.502.. Validation accuracy: 0.862 Epoch 3/7.. Train loss: 1.006.. Validation loss: 0.557.. Validation accuracy: 0.830 Epoch 3/7.. Train loss: 0.979.. Validation loss: 0.528.. Validation accuracy: 0.840 Epoch 3/7.. Train loss: 0.998.. Validation loss: 0.548.. Validation accuracy: 0.846 Epoch 3/7.. Train loss: 1.000.. Validation loss: 0.537.. Validation accuracy: 0.842 Epoch 3/7.. Train loss: 1.009.. Validation loss: 0.491.. Validation accuracy: 0.863 Epoch 3/7.. Train loss: 1.005.. Validation loss: 0.546.. Validation accuracy: 0.847 Epoch 3/7.. Train loss: 0.978.. Validation loss: 0.513.. Validation accuracy: 0.846 Epoch 3/7.. Train loss: 0.784.. Validation loss: 0.510.. Validation accuracy: 0.857 Epoch 4/7.. Train loss: 0.930.. Validation loss: 0.508.. Validation accuracy: 0.848 Epoch 4/7.. Train loss: 0.887.. Validation loss: 0.497.. Validation accuracy: 0.847 Epoch 4/7.. Train loss: 0.837.. Validation loss: 0.537.. Validation accuracy: 0.844 Epoch 4/7.. Train loss: 0.926.. Validation loss: 0.496.. Validation accuracy: 0.859 Epoch 4/7.. Train loss: 0.965.. Validation loss: 0.473.. Validation accuracy: 0.870 Epoch 4/7.. Train loss: 0.840.. Validation loss: 0.451.. Validation accuracy: 0.874 Epoch 4/7.. Train loss: 0.983.. Validation loss: 0.461.. Validation accuracy: 0.872 Epoch 4/7.. Train loss: 0.822.. Validation loss: 0.497.. Validation accuracy: 0.852 Epoch 4/7.. Train loss: 0.921.. Validation loss: 0.452.. Validation accuracy: 0.870 Epoch 4/7.. Train loss: 0.898.. Validation loss: 0.429.. Validation accuracy: 0.883 Epoch 4/7.. Train loss: 0.904.. Validation loss: 0.480.. Validation accuracy: 0.859 Epoch 4/7.. Train loss: 0.924.. Validation loss: 0.460.. Validation accuracy: 0.875 Epoch 4/7.. Train loss: 0.802.. Validation loss: 0.446.. Validation accuracy: 0.864 Epoch 4/7.. Train loss: 0.662.. Validation loss: 0.456.. Validation accuracy: 0.878 Epoch 4/7.. Train loss: 0.777.. Validation loss: 0.422.. Validation accuracy: 0.879 Epoch 4/7.. Train loss: 0.829.. Validation loss: 0.464.. Validation accuracy: 0.860 Epoch 4/7.. Train loss: 0.959.. Validation loss: 0.455.. Validation accuracy: 0.866 Epoch 4/7.. Train loss: 0.807.. Validation loss: 0.523.. Validation accuracy: 0.843 Epoch 4/7.. Train loss: 0.839.. Validation loss: 0.441.. Validation accuracy: 0.863 Epoch 4/7.. Train loss: 0.882.. Validation loss: 0.467.. Validation accuracy: 0.853 Epoch 4/7.. Train loss: 0.917.. Validation loss: 0.425.. Validation accuracy: 0.884 Epoch 5/7.. Train loss: 0.801.. Validation loss: 0.428.. Validation accuracy: 0.875 Epoch 5/7.. Train loss: 0.955.. Validation loss: 0.471.. Validation accuracy: 0.864 Epoch 5/7.. Train loss: 0.844.. Validation loss: 0.541.. Validation accuracy: 0.856 Epoch 5/7.. Train loss: 0.834.. Validation loss: 0.559.. Validation accuracy: 0.836 Epoch 5/7.. Train loss: 0.850.. Validation loss: 0.419.. Validation accuracy: 0.886 Epoch 5/7.. Train loss: 0.664.. Validation loss: 0.433.. Validation accuracy: 0.871 Epoch 5/7.. Train loss: 0.846.. Validation loss: 0.472.. Validation accuracy: 0.871 Epoch 5/7.. Train loss: 0.739.. Validation loss: 0.412.. Validation accuracy: 0.883 Epoch 5/7.. Train loss: 0.694.. Validation loss: 0.395.. Validation accuracy: 0.888 Epoch 5/7.. Train loss: 0.823.. Validation loss: 0.387.. Validation accuracy: 0.904 Epoch 5/7.. Train loss: 0.717.. Validation loss: 0.406.. Validation accuracy: 0.882 Epoch 5/7.. Train loss: 0.909.. Validation loss: 0.427.. Validation accuracy: 0.877 Epoch 5/7.. Train loss: 0.740.. Validation loss: 0.418.. Validation accuracy: 0.867 Epoch 5/7.. Train loss: 0.771.. Validation loss: 0.384.. Validation accuracy: 0.883 Epoch 5/7.. Train loss: 0.864.. Validation loss: 0.395.. Validation accuracy: 0.887 Epoch 5/7.. Train loss: 0.623.. Validation loss: 0.505.. Validation accuracy: 0.854 Epoch 5/7.. Train loss: 0.766.. Validation loss: 0.433.. Validation accuracy: 0.878 Epoch 5/7.. Train loss: 0.899.. Validation loss: 0.380.. Validation accuracy: 0.890 Epoch 5/7.. Train loss: 0.826.. Validation loss: 0.413.. Validation accuracy: 0.885 Epoch 5/7.. Train loss: 0.783.. Validation loss: 0.427.. Validation accuracy: 0.885 Epoch 5/7.. Train loss: 0.828.. Validation loss: 0.444.. Validation accuracy: 0.868 Epoch 6/7.. Train loss: 0.743.. Validation loss: 0.427.. Validation accuracy: 0.869 Epoch 6/7.. Train loss: 0.783.. Validation loss: 0.401.. Validation accuracy: 0.880 Epoch 6/7.. Train loss: 0.688.. Validation loss: 0.411.. Validation accuracy: 0.875 Epoch 6/7.. Train loss: 0.756.. Validation loss: 0.443.. Validation accuracy: 0.872 Epoch 6/7.. Train loss: 0.706.. Validation loss: 0.376.. Validation accuracy: 0.886 Epoch 6/7.. Train loss: 0.830.. Validation loss: 0.395.. Validation accuracy: 0.899 Epoch 6/7.. Train loss: 0.834.. Validation loss: 0.417.. Validation accuracy: 0.880 Epoch 6/7.. Train loss: 0.812.. Validation loss: 0.460.. Validation accuracy: 0.860 Epoch 6/7.. Train loss: 0.748.. Validation loss: 0.348.. Validation accuracy: 0.901 Epoch 6/7.. Train loss: 0.600.. Validation loss: 0.352.. Validation accuracy: 0.903 Epoch 6/7.. Train loss: 0.650.. Validation loss: 0.359.. Validation accuracy: 0.900 Epoch 6/7.. Train loss: 0.666.. Validation loss: 0.404.. Validation accuracy: 0.892 Epoch 6/7.. Train loss: 0.648.. Validation loss: 0.407.. Validation accuracy: 0.889 Epoch 6/7.. Train loss: 0.814.. Validation loss: 0.366.. Validation accuracy: 0.902 Epoch 6/7.. Train loss: 0.666.. Validation loss: 0.387.. Validation accuracy: 0.884 Epoch 6/7.. Train loss: 0.822.. Validation loss: 0.429.. Validation accuracy: 0.883 Epoch 6/7.. Train loss: 0.850.. Validation loss: 0.385.. Validation accuracy: 0.887 Epoch 6/7.. Train loss: 0.762.. Validation loss: 0.382.. Validation accuracy: 0.884 Epoch 6/7.. Train loss: 0.631.. Validation loss: 0.381.. Validation accuracy: 0.887 Epoch 6/7.. Train loss: 0.660.. Validation loss: 0.398.. Validation accuracy: 0.881 Epoch 7/7.. Train loss: 0.695.. Validation loss: 0.455.. Validation accuracy: 0.873 Epoch 7/7.. Train loss: 0.665.. Validation loss: 0.437.. Validation accuracy: 0.873 Epoch 7/7.. Train loss: 0.625.. Validation loss: 0.354.. Validation accuracy: 0.905 Epoch 7/7.. Train loss: 0.784.. Validation loss: 0.390.. Validation accuracy: 0.883 Epoch 7/7.. Train loss: 0.603.. Validation loss: 0.360.. Validation accuracy: 0.896 Epoch 7/7.. Train loss: 0.781.. Validation loss: 0.364.. Validation accuracy: 0.907 Epoch 7/7.. Train loss: 0.705.. Validation loss: 0.376.. Validation accuracy: 0.899 Epoch 7/7.. Train loss: 0.704.. Validation loss: 0.378.. Validation accuracy: 0.904 Epoch 7/7.. Train loss: 0.638.. Validation loss: 0.374.. Validation accuracy: 0.890 Epoch 7/7.. Train loss: 0.644.. Validation loss: 0.364.. Validation accuracy: 0.903 Epoch 7/7.. Train loss: 0.637.. Validation loss: 0.401.. Validation accuracy: 0.876 Epoch 7/7.. Train loss: 0.609.. Validation loss: 0.374.. Validation accuracy: 0.894 Epoch 7/7.. Train loss: 0.664.. Validation loss: 0.351.. Validation accuracy: 0.904 Epoch 7/7.. Train loss: 0.756.. Validation loss: 0.382.. Validation accuracy: 0.896 Epoch 7/7.. Train loss: 0.694.. Validation loss: 0.412.. Validation accuracy: 0.883 Epoch 7/7.. Train loss: 0.753.. Validation loss: 0.366.. Validation accuracy: 0.903 Epoch 7/7.. Train loss: 0.543.. Validation loss: 0.374.. Validation accuracy: 0.899 Epoch 7/7.. Train loss: 0.748.. Validation loss: 0.403.. Validation accuracy: 0.884 Epoch 7/7.. Train loss: 0.697.. Validation loss: 0.385.. Validation accuracy: 0.897 Epoch 7/7.. Train loss: 0.624.. Validation loss: 0.386.. Validation accuracy: 0.882 Epoch 7/7.. Train loss: 0.663.. Validation loss: 0.359.. Validation accuracy: 0.896
accuracy on the testing set more or less platoes after epoch 5
Testing your network¶
It's good practice to test your trained network on test data, images the network has never seen either in training or validation. This will give you a good estimate for the model's performance on completely new images. Run the test images through the network and measure the accuracy, the same way you did validation. You should be able to reach around 70% accuracy on the test set if the model has been trained well.
In [16]:
# TODO: Do validation on the test set
"""
Source: Udacity Deep Learning with Pytorch lesson
"""
def test_Model():
inputs, labels = next(iter(testloader))
inputs, labels = inputs.to(device), labels.to(device)
# Get the class probabilities
ps = torch.exp(model(inputs))
# Make sure the shape is appropriate, we should get 102 class probabilities for 64 examples
print(ps.shape)
top_p, top_class = ps.topk(1, dim=1)
# Look at the most likely classes for the first 10 examples
print(top_class[:10,:])
equals = top_class == labels.view(*top_class.shape)
accuracy = torch.mean(equals.type(torch.FloatTensor))
print(f'Accuracy: {accuracy.item()*100}%')
In [19]:
test_Model()
torch.Size([64, 102])
tensor([[ 72],
[ 8],
[ 89],
[ 61],
[ 73],
[ 100],
[ 24],
[ 62],
[ 92],
[ 81]], device='cuda:0')
Accuracy: 92.1875%
Save the checkpoint¶
Now that your network is trained, save the model so you can load it later for making predictions. You probably want to save other things such as the mapping of classes to indices which you get from one of the image datasets: image_datasets['train'].class_to_idx. You can attach this to the model as an attribute which makes inference easier later on.
model.class_to_idx = image_datasets['train'].class_to_idx
Remember that you'll want to completely rebuild the model later so you can use it for inference. Make sure to include any information you need in the checkpoint. If you want to load the model and keep training, you'll want to save the number of epochs as well as the optimizer state, optimizer.state_dict. You'll likely want to use this trained model in the next part of the project, so best to save it now.
Links related to saving and loading a checkpoint
https://pytorch.org/docs/master/notes/serialization.html
https://pytorch.org/tutorials/beginner/saving_loading_models.html
https://discuss.pytorch.org/t/problem-loading-model-trained-on-gpu/17745
In [23]:
model.class_to_idx = train_data.class_to_idx
In [24]:
model.class_to_idx
Out[24]:
{'1': 0,
'10': 1,
'100': 2,
'101': 3,
'102': 4,
'11': 5,
'12': 6,
'13': 7,
'14': 8,
'15': 9,
'16': 10,
'17': 11,
'18': 12,
'19': 13,
'2': 14,
'20': 15,
'21': 16,
'22': 17,
'23': 18,
'24': 19,
'25': 20,
'26': 21,
'27': 22,
'28': 23,
'29': 24,
'3': 25,
'30': 26,
'31': 27,
'32': 28,
'33': 29,
'34': 30,
'35': 31,
'36': 32,
'37': 33,
'38': 34,
'39': 35,
'4': 36,
'40': 37,
'41': 38,
'42': 39,
'43': 40,
'44': 41,
'45': 42,
'46': 43,
'47': 44,
'48': 45,
'49': 46,
'5': 47,
'50': 48,
'51': 49,
'52': 50,
'53': 51,
'54': 52,
'55': 53,
'56': 54,
'57': 55,
'58': 56,
'59': 57,
'6': 58,
'60': 59,
'61': 60,
'62': 61,
'63': 62,
'64': 63,
'65': 64,
'66': 65,
'67': 66,
'68': 67,
'69': 68,
'7': 69,
'70': 70,
'71': 71,
'72': 72,
'73': 73,
'74': 74,
'75': 75,
'76': 76,
'77': 77,
'78': 78,
'79': 79,
'8': 80,
'80': 81,
'81': 82,
'82': 83,
'83': 84,
'84': 85,
'85': 86,
'86': 87,
'87': 88,
'88': 89,
'89': 90,
'9': 91,
'90': 92,
'91': 93,
'92': 94,
'93': 95,
'94': 96,
'95': 97,
'96': 98,
'97': 99,
'98': 100,
'99': 101}
In [20]:
print("Our model: \n\n", model, '\n')
print("The state dict keys: \n\n", model.state_dict().keys())
Our model:
VGG(
(features): Sequential(
(0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace)
(3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(5): ReLU(inplace)
(6): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(7): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(8): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(9): ReLU(inplace)
(10): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(11): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(12): ReLU(inplace)
(13): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(14): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(15): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(16): ReLU(inplace)
(17): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(18): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(19): ReLU(inplace)
(20): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(21): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(22): ReLU(inplace)
(23): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(24): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(25): ReLU(inplace)
(26): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(27): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(28): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(29): ReLU(inplace)
(30): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(31): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(32): ReLU(inplace)
(33): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(34): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(35): ReLU(inplace)
(36): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(37): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(38): ReLU(inplace)
(39): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(40): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(41): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(42): ReLU(inplace)
(43): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(44): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(45): ReLU(inplace)
(46): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(47): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(48): ReLU(inplace)
(49): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(50): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(51): ReLU(inplace)
(52): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
)
(classifier): Sequential(
(0): Linear(in_features=25088, out_features=4096, bias=True)
(1): ReLU()
(2): Dropout(p=0.2)
(3): Linear(in_features=4096, out_features=1024, bias=True)
(4): ReLU()
(5): Dropout(p=0.2)
(6): Linear(in_features=1024, out_features=102, bias=True)
(7): LogSoftmax()
)
)
The state dict keys:
odict_keys(['features.0.weight', 'features.0.bias', 'features.1.weight', 'features.1.bias', 'features.1.running_mean', 'features.1.running_var', 'features.3.weight', 'features.3.bias', 'features.4.weight', 'features.4.bias', 'features.4.running_mean', 'features.4.running_var', 'features.7.weight', 'features.7.bias', 'features.8.weight', 'features.8.bias', 'features.8.running_mean', 'features.8.running_var', 'features.10.weight', 'features.10.bias', 'features.11.weight', 'features.11.bias', 'features.11.running_mean', 'features.11.running_var', 'features.14.weight', 'features.14.bias', 'features.15.weight', 'features.15.bias', 'features.15.running_mean', 'features.15.running_var', 'features.17.weight', 'features.17.bias', 'features.18.weight', 'features.18.bias', 'features.18.running_mean', 'features.18.running_var', 'features.20.weight', 'features.20.bias', 'features.21.weight', 'features.21.bias', 'features.21.running_mean', 'features.21.running_var', 'features.23.weight', 'features.23.bias', 'features.24.weight', 'features.24.bias', 'features.24.running_mean', 'features.24.running_var', 'features.27.weight', 'features.27.bias', 'features.28.weight', 'features.28.bias', 'features.28.running_mean', 'features.28.running_var', 'features.30.weight', 'features.30.bias', 'features.31.weight', 'features.31.bias', 'features.31.running_mean', 'features.31.running_var', 'features.33.weight', 'features.33.bias', 'features.34.weight', 'features.34.bias', 'features.34.running_mean', 'features.34.running_var', 'features.36.weight', 'features.36.bias', 'features.37.weight', 'features.37.bias', 'features.37.running_mean', 'features.37.running_var', 'features.40.weight', 'features.40.bias', 'features.41.weight', 'features.41.bias', 'features.41.running_mean', 'features.41.running_var', 'features.43.weight', 'features.43.bias', 'features.44.weight', 'features.44.bias', 'features.44.running_mean', 'features.44.running_var', 'features.46.weight', 'features.46.bias', 'features.47.weight', 'features.47.bias', 'features.47.running_mean', 'features.47.running_var', 'features.49.weight', 'features.49.bias', 'features.50.weight', 'features.50.bias', 'features.50.running_mean', 'features.50.running_var', 'classifier.0.weight', 'classifier.0.bias', 'classifier.3.weight', 'classifier.3.bias', 'classifier.6.weight', 'classifier.6.bias'])
In [21]:
model.state_dict ()
Out[21]:
OrderedDict([('features.0.weight',
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In [69]:
# save state dict only
# torch.save(model.state_dict(), 'checkpoint_image_classifier_state_dict.pth')
In [18]:
"""save checkpoint
I've commented it out not to accidentally overwrite my existing checkpoint of the model I trained
I've also backed up the checkpoint
"""
# model.to('cpu')
# torch.save({
# 'model_state_dict': model.state_dict(),
# 'optimizer_state_dict': optimizer.state_dict(),
# 'classifier': model.classifier,
# 'structure': 'vgg19_bn',
# 'epochs': epochs,
# 'class_to_idx': model.class_to_idx
# }, 'checkpoint_image_classifier.pth')
Out[18]:
"save checkpoint\nI've commented it out not to accidentally overwrite my existing checkpoint\nI've also backed up the checkpoint\n"
Loading the checkpoint¶
At this point it's good to write a function that can load a checkpoint and rebuild the model. That way you can come back to this project and keep working on it without having to retrain the network.
In [ ]:
# load state dict only
# PATH_state_dict = 'checkpoint_image_classifier_state_dict.pth'
# model.load_state_dict(torch.load(PATH_state_dict))
# model.eval()
In [7]:
def load_Model():
global model, device
# Use GPU if it's available
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = models.vgg19_bn()
criterion = nn.NLLLoss()
PATH = 'checkpoint_image_classifier.pth'
#https://discuss.pytorch.org/t/problem-loading-model-trained-on-gpu/17745
try:
checkpoint = torch.load(PATH)
except:
checkpoint = torch.load(PATH, map_location=lambda storage, loc: storage)
optimizer = optim.Adam(model.classifier.parameters(), lr=0.001)
model.optimizer_state_dict = checkpoint['optimizer_state_dict']
model.classifier = checkpoint['classifier']
model.load_state_dict(checkpoint['model_state_dict'])
optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
model.class_to_idx = checkpoint['class_to_idx']
model.to(device);
model.eval()
load_Model()
In [17]:
# did I load the model?
model
Out[17]:
VGG(
(features): Sequential(
(0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace)
(3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(5): ReLU(inplace)
(6): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(7): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(8): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(9): ReLU(inplace)
(10): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(11): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(12): ReLU(inplace)
(13): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(14): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(15): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(16): ReLU(inplace)
(17): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(18): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(19): ReLU(inplace)
(20): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(21): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(22): ReLU(inplace)
(23): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(24): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(25): ReLU(inplace)
(26): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(27): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(28): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(29): ReLU(inplace)
(30): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(31): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(32): ReLU(inplace)
(33): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(34): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(35): ReLU(inplace)
(36): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(37): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(38): ReLU(inplace)
(39): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(40): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(41): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(42): ReLU(inplace)
(43): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(44): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(45): ReLU(inplace)
(46): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(47): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(48): ReLU(inplace)
(49): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(50): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(51): ReLU(inplace)
(52): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
)
(classifier): Sequential(
(0): Linear(in_features=25088, out_features=4096, bias=True)
(1): ReLU()
(2): Dropout(p=0.2)
(3): Linear(in_features=4096, out_features=1024, bias=True)
(4): ReLU()
(5): Dropout(p=0.2)
(6): Linear(in_features=1024, out_features=102, bias=True)
(7): LogSoftmax()
)
)
In [18]:
model.state_dict ()
Out[18]:
OrderedDict([('features.0.weight',
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In [27]:
# testing the model accuracy again to make sure it loaded fine
# this only appears to work when I'm in GPU mode. On CPU, it takes forever to respond, I had to interrupt the kernet.
# So only test it in GPU
# if you wanna test it on CPU, run individual files thru the model, it works fine
model.to(device)
test_Model()
torch.Size([64, 102])
tensor([[ 74],
[ 32],
[ 70],
[ 55],
[ 3],
[ 76],
[ 96],
[ 52],
[ 54],
[ 78]], device='cuda:0')
Accuracy: 85.9375%
In [19]:
torch.cuda.is_available()
Out[19]:
False
Inference for classification¶
Now you'll write a function to use a trained network for inference. That is, you'll pass an image into the network and predict the class of the flower in the image. Write a function called predict that takes an image and a model, then returns the top $K$ most likely classes along with the probabilities. It should look like
probs, classes = predict(image_path, model)
print(probs)
print(classes)
> [ 0.01558163 0.01541934 0.01452626 0.01443549 0.01407339]
> ['70', '3', '45', '62', '55']
First you'll need to handle processing the input image such that it can be used in your network.
Image Preprocessing¶
You'll want to use PIL to load the image (documentation). It's best to write a function that preprocesses the image so it can be used as input for the model. This function should process the images in the same manner used for training.
First, resize the images where the shortest side is 256 pixels, keeping the aspect ratio. This can be done with the thumbnail or resize methods. Then you'll need to crop out the center 224x224 portion of the image.
Color channels of images are typically encoded as integers 0-255, but the model expected floats 0-1. You'll need to convert the values. It's easiest with a Numpy array, which you can get from a PIL image like so np_image = np.array(pil_image).
As before, the network expects the images to be normalized in a specific way. For the means, it's [0.485, 0.456, 0.406] and for the standard deviations [0.229, 0.224, 0.225]. You'll want to subtract the means from each color channel, then divide by the standard deviation.
And finally, PyTorch expects the color channel to be the first dimension but it's the third dimension in the PIL image and Numpy array. You can reorder dimensions using ndarray.transpose. The color channel needs to be first and retain the order of the other two dimensions.
In [8]:
def process_image(image):
''' Open, transform (resize, centre crop, normalize (between zero and 1 ), convert to a Numpy array)
'''
# Converting image to PIL image using image file path
PIL_Image = Image.open(f'{image}')
# Building image transform
"""this transform is taken from Udacit intro to Pytorch lesson"""
transform = transforms.Compose([transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406],
[0.229, 0.224, 0.225])])
## Transforming image for use with network
PIL_Transformed = transform(PIL_Image)
# Converting to Numpy array
"""https://kite.com/python/examples/4887/pil-convert-between-a-pil-%60image%60-and-a-numpy-%60array%60"""
numpy_array = np.array(PIL_Transformed)
return numpy_array
In [9]:
def imshow(image, ax=None, title=None):
if ax is None:
fig, ax = plt.subplots()
# PyTorch tensors assume the color channel is the first dimension
# but matplotlib assumes is the third dimension
#print('image shape before transpost', image.shape)
image = image.transpose((1, 2, 0))
#print('image shape after transpose', image.shape)
# Undo preprocessing
mean = np.array([0.485, 0.456, 0.406])
std = np.array([0.229, 0.224, 0.225])
image = std * image + mean
# Image needs to be clipped between 0 and 1 or it looks like noise when displayed
image = np.clip(image, 0, 1)
ax.imshow(image)
return ax
In [10]:
# Test image
image_name = data_dir + '/test' + '/1/' + 'image_06743.jpg'
img_test = process_image(image_name)
# Check to see if image pre-processing was successful
imshow(img_test, ax=None, title=None)
Out[10]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f620631e4e0>
In [11]:
# Test a bunch of images
for image in glob.glob((data_dir + '/test/1/*')):
print (image)
my_processed_image = process_image(image)
imshow(my_processed_image)
flowers/test/1/image_06760.jpg flowers/test/1/image_06754.jpg flowers/test/1/image_06743.jpg flowers/test/1/image_06764.jpg flowers/test/1/image_06752.jpg
In [17]:
imshow(process_image('flowers/test/1/image_06764.jpg'))
Out[17]:
<matplotlib.axes._subplots.AxesSubplot at 0x7fa79816af28>
To check your work, the function below converts a PyTorch tensor and displays it in the notebook. If your process_image function works, running the output through this function should return the original image (except for the cropped out portions).
Class Prediction¶
Once you can get images in the correct format, it's time to write a function for making predictions with your model. A common practice is to predict the top 5 or so (usually called top-$K$) most probable classes. You'll want to calculate the class probabilities then find the $K$ largest values.
To get the top $K$ largest values in a tensor use x.topk(k). This method returns both the highest k probabilities and the indices of those probabilities corresponding to the classes. You need to convert from these indices to the actual class labels using class_to_idx which hopefully you added to the model or from an ImageFolder you used to load the data (see here). Make sure to invert the dictionary so you get a mapping from index to class as well.
Again, this method should take a path to an image and a model checkpoint, then return the probabilities and classes.
probs, classes = predict(image_path, model)
print(probs)
print(classes)
> [ 0.01558163 0.01541934 0.01452626 0.01443549 0.01407339]
> ['70', '3', '45', '62', '55']
In [12]:
def predict(image_path, model=model, topk=5):
''' Predict flower species.
To be honest, I struggled with this part and got stuck so badly, that I had to take a peek at waht other students had submitted
The code inn this cell is adapted from:
ttps://github.com/S-Tabor/udacity-image-classifier-project/blob/master/Image%20Classifier%20Project.ipynb
by S-Tabor
I didn't copy paste the code, but I got the general idea for implementation.
I made sure to avoid plagiarism
https://udacity.zendesk.com/hc/en-us/articles/360001451091-What-is-plagiarism-
Not Plagiarism:
Looking at someone else’s code to get a general idea of implementation, then putting it away and starting to write your own code from scratch.
'''
#process the image
img = process_image(image_path)
# Converting to torch tensor from Numpy array
#https://discuss.pytorch.org/t/how-to-convert-array-to-tensor/28809
#https://discuss.pytorch.org/t/difference-between-tensor-and-torch-floattensor/24448/2
img_tensor = torch.from_numpy(img).type(torch.FloatTensor)
# Adding a dimension to image
#https://discuss.pytorch.org/t/what-is-the-difference-between-view-and-unsqueeze/1155
img_add_dim = img_tensor.unsqueeze_(0)
# Setting model to evaluation mode and turning off gradients to speed up the following step
model.eval()
with torch.no_grad():
# Running image through network
try:
output = model(img_add_dim)
except:
output = model(img_add_dim.cuda())
# probabilities
probabilities = torch.exp(output)
# print(probabilities)
top_probabilities = probabilities.topk(topk)[0]
top_indexes = probabilities.topk(topk)[1]
# Converting probabilities and outputs to numpy arrays
probs_top_list = np.array(top_probabilities)[0]
# print(type(probs_top_list))
index_top_list = np.array(top_indexes[0])
# Loading index and class mapping
class_to_idx = model.class_to_idx
# Inverting index-class dictionary
#https://stackoverflow.com/questions/483666/reverse-invert-a-dictionary-mapping
indx_to_class = {A: B for B, A in class_to_idx.items()}
# Converting index list to class list
classes_top_list = []
for index in index_top_list:
classes_top_list += [indx_to_class[index]]
print(image_path)
print('top probs', probs_top_list)
print('top_classes',classes_top_list)
return probs_top_list, classes_top_list
# testing if the actual label which I entered matches the predicted one
# import glob
# for filepath in glob.glob((data_dir + '/test/96/*')):
# predict(filepath, model=model, topk=5)
In [13]:
predict('flowers/test/30/image_03481.jpg')
flowers/test/30/image_03481.jpg top probs [ 9.86215413e-01 1.26778614e-02 6.38392114e-04 1.63586883e-04 1.45571292e-04] top_classes ['30', '27', '72', '92', '82']
Out[13]:
(array([ 9.86215413e-01, 1.26778614e-02, 6.38392114e-04,
1.63586883e-04, 1.45571292e-04], dtype=float32),
['30', '27', '72', '92', '82'])
In [14]:
for name in glob.glob((data_dir + '/test/66/*')):
predict(name, topk=1)
flowers/test/66/image_05537.jpg top probs [ 0.99731314] top_classes ['66'] flowers/test/66/image_05582.jpg top probs [ 0.99545413] top_classes ['66'] flowers/test/66/image_05562.jpg top probs [ 0.99719405] top_classes ['66'] flowers/test/66/image_05549.jpg top probs [ 0.99983472] top_classes ['66']
In [31]:
Myfilepath='flowers/test/30/image_03538.jpg'
# print(Myfilepath)
predict(image_path=Myfilepath, model=model, topk=1)
flowers/test/30/image_03538.jpg top probs [ 0.99615127] top_classes ['30']
Out[31]:
(array([ 0.99615127], dtype=float32), ['30'])
In [32]:
cat_to_name['1']
Out[32]:
'pink primrose'
In [33]:
for image in glob.glob((data_dir + '/test/64/*')):
my_processed_image = process_image(image)
imshow(my_processed_image)
predict(image)
flowers/test/64/image_06099.jpg top probs [ 9.94310141e-01 1.56016741e-03 1.09841069e-03 7.83293741e-04 6.93015987e-04] top_classes ['64', '32', '53', '51', '76'] flowers/test/64/image_06130.jpg top probs [ 9.84275758e-01 1.23303989e-02 2.64858012e-03 3.03940789e-04 1.85715384e-04] top_classes ['64', '51', '76', '32', '65'] flowers/test/64/image_06138.jpg top probs [ 0.97947115 0.00956609 0.00597732 0.0016611 0.00158365] top_classes ['64', '65', '67', '42', '34'] flowers/test/64/image_06104.jpg top probs [ 0.73628443 0.20635158 0.03331089 0.01019444 0.00437504] top_classes ['64', '76', '55', '19', '65'] flowers/test/64/image_06134.jpg top probs [ 9.45744574e-01 4.98634055e-02 2.02328758e-03 1.39389327e-03 4.48154111e-04] top_classes ['64', '76', '34', '65', '42']
In [26]:
predict('flowers/test/1/image_06760.jpg')
flowers/test/1/image_06760.jpg top probs [ 0.4002547 0.34357026 0.09689626 0.08575003 0.0340166 ] top_classes ['72', '95', '40', '96', '97']
Out[26]:
(array([ 0.4002547 , 0.34357026, 0.09689626, 0.08575003, 0.0340166 ], dtype=float32), ['72', '95', '40', '96', '97'])
Sanity Checking¶
Now that you can use a trained model for predictions, check to make sure it makes sense. Even if the testing accuracy is high, it's always good to check that there aren't obvious bugs. Use matplotlib to plot the probabilities for the top 5 classes as a bar graph, along with the input image. It should look like this:
You can convert from the class integer encoding to actual flower names with the cat_to_name.json file (should have been loaded earlier in the notebook). To show a PyTorch tensor as an image, use the imshow function defined above.
In [16]:
# cat_to_name
In [15]:
def check_sanity(folder = 30, image_number = '03466'):
#https://matplotlib.org/3.1.0/gallery/pyplots/pyplot_two_subplots.html
#https://stackoverflow.com/questions/24644656/how-to-print-pandas-dataframe-without-index
#https://stackoverflow.com/questions/30522724/take-multiple-lists-into-dataframe
path = test_dir + '/' +str(folder)+ '/image_' + image_number +'.jpg'
image = process_image(path)
probabilities, classes = predict(path, model)
class_names = []
for i in classes:
class_names += [cat_to_name[i]]
df = pd.DataFrame(
{'classes': classes,
'class_names': class_names,
'probabilities': probabilities
})
plt.rcParams["figure.figsize"] = (10,5)
plt.figure()
title = cat_to_name[str(folder)]
ax0 =imshow(image)
ax0.set_title(title)
plt.figure(figsize = (10,5))
ax1 = sns.barplot(y="class_names", x="probabilities", data=df)
plt.show()
print (df.to_string(index=False))
check_sanity()
flowers/test/30/image_03466.jpg top probs [ 9.96831357e-01 2.07501207e-03 8.09328572e-04 1.29197128e-04 5.72931094e-05] top_classes ['30', '82', '32', '38', '1']
<matplotlib.figure.Figure at 0x7f6202899668>
classes class_names probabilities
30 sweet william 0.996831
82 clematis 0.002075
32 garden phlox 0.000809
38 great masterwort 0.000129
1 pink primrose 0.000057
In [18]:
check_sanity(folder = 30, image_number = '03528')
flowers/test/30/image_03528.jpg top probs [ 9.99993920e-01 4.25792041e-06 1.29685350e-06 2.85470236e-07 1.49205505e-07] top_classes ['30', '51', '94', '32', '82']
<matplotlib.figure.Figure at 0x7f6202974710>
classes class_names probabilities
30 sweet william 9.999939e-01
51 petunia 4.257920e-06
94 foxglove 1.296853e-06
32 garden phlox 2.854702e-07
82 clematis 1.492055e-07
In [117]:
for name in glob.glob((data_dir + '/test/66/*')):
print(name)
flowers/test/66/image_05537.jpg flowers/test/66/image_05582.jpg flowers/test/66/image_05562.jpg flowers/test/66/image_05549.jpg
In [17]:
def check_sanity_whole_folder(folder = 1):
#https://matplotlib.org/3.1.0/gallery/pyplots/pyplot_two_subplots.html
#https://stackoverflow.com/questions/24644656/how-to-print-pandas-dataframe-without-index
#https://stackoverflow.com/questions/30522724/take-multiple-lists-into-dataframe
for name in glob.glob((data_dir + '/test/'+ str(folder) + '/*')):
path = name
image = process_image(path)
probabilities, classes = predict(path, model)
class_names = []
for i in classes:
class_names += [cat_to_name[i]]
df = pd.DataFrame(
{'classes': classes,
'class_names': class_names,
'probabilities': probabilities
})
plt.rcParams["figure.figsize"] = (10,5)
plt.figure()
title = cat_to_name[str(folder)]
ax0 =imshow(image)
ax0.set_title(title)
plt.figure(figsize = (10,5))
ax1 = sns.barplot(y="class_names", x="probabilities", data=df)
plt.show()
In [31]:
# check_sanity_whole_folder()
hard ones:¶
'4': 'sweet pea' - got most of them wrong
'53': 'primula' is a hard one to recognize, there were quite a few error
'83': 'hibiscus' is a relative hard one too, they look different, different colours
easy ones:¶
'71': 'gazania' - confidently gets all of them right
'25': 'grape hyacinth',
'66': 'osteospermum',
'13': 'king protea',
In [19]:
check_sanity_whole_folder(13)
flowers/test/13/image_05767.jpg top probs [ 9.91392374e-01 3.21134622e-03 2.31901277e-03 1.11253397e-03 5.08167141e-04] top_classes ['13', '18', '29', '73', '39']
<matplotlib.figure.Figure at 0x7f62029d26d8>
flowers/test/13/image_05775.jpg top probs [ 9.99929130e-01 4.18586933e-05 1.00311699e-05 6.85777741e-06 4.88819569e-06] top_classes ['13', '29', '21', '77', '18']
<matplotlib.figure.Figure at 0x7f620634e908>
flowers/test/13/image_05761.jpg top probs [ 5.42159617e-01 4.56748009e-01 9.99706215e-04 8.57696068e-05 3.20615459e-06] top_classes ['13', '29', '73', '18', '31']
<matplotlib.figure.Figure at 0x7f62029dedd8>
flowers/test/13/image_05745.jpg top probs [ 9.99884248e-01 3.12176489e-05 2.58994496e-05 2.29517900e-05 1.19559218e-05] top_classes ['13', '92', '100', '41', '29']
<matplotlib.figure.Figure at 0x7f62042f42e8>
flowers/test/13/image_05769.jpg top probs [ 9.99045432e-01 7.12506997e-04 2.08429396e-04 1.57285976e-05 4.92571235e-06] top_classes ['13', '18', '91', '77', '73']
<matplotlib.figure.Figure at 0x7f62028e2550>
flowers/test/13/image_05787.jpg top probs [ 0.86515272 0.11042858 0.01281158 0.00439596 0.00217577] top_classes ['13', '29', '91', '39', '18']
<matplotlib.figure.Figure at 0x7f620261ef60>
